1. Field
This disclosure is generally related to activity-based recommender systems. More specifically, this disclosure is related to determining and using a probability threshold to make a decision on whether a user is performing a certain action.
2. Related Art
Advances in mobile computing have allowed people to consume digital information at any place and any time. It is common for laptops to boast a sufficiently long battery life to allow a user to complete a near full day of work on a single charge. If the user needs an Internet connection, the user is oftentimes able to find a public Wi-Fi hot spot at a local coffee shop or a public venue such as a park. As another example, the capabilities of smartphones have increased drastically to rival the computing abilities of laptops, while also providing steady access to the Internet through a cellular network. These advances have allowed users to perform their computing tasks at a coffee shop, on a park bench, or virtually anywhere. Oftentimes, a user may take his mobile phone out from his pocket for brief moments at a time to play a quick game while riding in a bus, to read the news while waiting in line at a store, or to search for information as necessary.
However, this mobile nature of modern computing has made it difficult to provide targeted recommendations to a user. Previous recommender systems expected a user to be using a computer at home or at work, with an open mind to consider recommendations. A modern computer user is constantly on the go, and may look at his mobile device for short periods at a time. The user may accept a recommendation if it matches his current activity, and may ignore a recommendation that isn't appealing or convenient considering his current activity.
For example, a typical recommender system may analyze behavior patterns for many individual users to make a recommendation that is targeted to a certain user. The recommender system may group multiple users that have a similar behavior pattern into a group, and may recommend a product or a service to an individual user based on purchases made by other users in his group. One example of this recommender system includes the movie recommendation system used by Netflix, Inc. to recommend movies to a viewer based on the movie ratings of other similar users. Another example is the product recommendation system used by Amazon, Inc. to recommend products to the user as the user browses Amazon's online catalog. However, these recommendations may not appeal to the user if they don't reach the user at an appropriate time. A user may be receptive to a coupon or advertisement for a pizzeria if he is at a mall with some friends, but may ignore the coupon if he receives it during or after eating dinner with his friends at a diner.
Other recommender systems compute acceptance-probabilities for one or more alternative recommendations, and choose a recommendation that has a highest acceptance-probability. However, if all the recommendations have a low acceptance probability, then it does not make sense to send any recommendation to the user.
Making a recommendation is a deterministic action. While there is a plethora of work of late on predicting the probability of user activities, little consideration has been given to whether to trigger a recommendation action based on the activity probability. The probability threshold for an activity-prediction model can have a significant impact on the accuracy of the recommendation decision, even if the activity-prediction model accurately predicts the probability of the target activity occurring. If the probability threshold is too low, many low-probability activities may be incorrectly predicted to take place. If the probability threshold is too high, many high-probability activities may be incorrectly predicted to not take place. This trade-off is easy to overlook. Often, users manually set a threshold, either on a random basis, or based on their subjective knowledge, which is often imprecise and incomplete.